A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability

To accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. The prediction variance ratio test method is introduced to determine the optimal dimension of the VSVs. The VSV-BDA model is constructed through the use of VSVs and the optimal dimension on the basis of the training samples, which are divided from the collected samples into three situations having different numbers. ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering.

Predicting the Probability of Helper T Cell Immunodominant Sites through Discriminant Analysis

Bayesian discriminant analysis is used to predict whether or not a given protein segment will activate helper T cells. The predictor variables are drawn from the products of frequencies of amino acid residues. The model's predictive validity compares favourably with that of alternative modelling strategies, suggesting that this approach merits further investigation.